VII. CONCLUSION
In this paper, we have introduced an approach for automatically discovering patterns in successful team members’
character attribute development in team games. We ﬁrst model
the team members’ character attribute development using
attribute time series. We then cluster the standardized time
series of attributes into two clusters: time series indicative of
fast attribute growth rates and time series indicative of slow
attribute growth rates. Linear regression is used to ﬁnd the
growth rate values of cluster centroids of both the fast cluster
and the slow cluster. Finally, we characterize the patterns of the
team success in terms of conjunctions of categorical attribute
growth rates by building a decision tree model. The enemy
team composition and players performance impact the attribute
growth rates. For example, a team of players play differently
when they face a different enemy team composition, which is
reﬂected by the game logs. Since our method is based on the
game logs, the impact of different enemy team compositions
and players performance is considered completely.
In this work we opted to use just two growth rates (fast
and slow) for our analysis; however, there is no limitation
in the technique that requires it. While the results we got
using just two growth rates were highly accurate, it would
be interesting for future work to examine the effects of using
different numbers of growth rate clusters.
We have shown it is possible to discover the patterns
of the team success in terms of conjunctions of categorical
attribute growth rates in team games using data only. The
only knowledge engineering in our method involves formatting
the data properly and contains no value judgements or expert
opinions. By moving away from knowledge-based methods,
we can make post-competition analysis for players more
efﬁcient. With our technique, they can easily investigate which
characters do harm to other characters. This is hard to ﬁg